The emergence of artificial intelligence in healthcare
Throughout the past decade with the emergence of artificial intelligence in healthcare, one truth remained constant: Despite all the hype, AI-focused startups and established tech companies alike have failed to move the needle on the nation’s overall health and medical costs.
Finally, after a decade of underperformance in AI-driven medicine, success is approaching faster than physicians and patients currently recognize.
The reason is ChatGPT, the generative AI chatbot from OpenAI that’s taking the digital world by storm
Since its launch in late November, ChatGPT has accomplished impressive feats—passing graduate-level exams for business, law and medical school (the answers to which can’t simply be Googled).
The next version, ChatGPT4, is scheduled for release later this year, as is Google’s rival AI product. And, last week, Microsoft unveiled an AI-powered search engine and web browser in partnership with OpenAI, with other tech-industry competitors slated to join the fray.
It remains to be seen which company will ultimately win the generative-AI arms race. But regardless of who comes out on top, we’ve reached a tipping point.
Generative AI will transform medicine as we know it
In the same way the iPhone became an essential part of our lives in what seemed like no time, ChatGPT (or whatever generative AI tool leads the way) will alter medical practice in previously unimaginable ways.
1. By becoming exponentially faster and more powerful
The human brain can easily predict the rate of arithmetic growth (whereby numbers increase at a constant rate: 1, 2, 3, 4). And it does reasonably well at comprehending geometric growth (a pattern that increases at a constant ratio: 1, 3, 9, 27), as well.
But the implications of continuous, exponential growth prove harder for the human mind to grasp. When it comes to generative AI, that’s the rate of growth to focus on.
Let’s assume that the power and speed of this new technology were to follow Moore’s Law, a posit that computational progress doubles roughly every two years. In that case, ChatGPT will be 32 times more powerful in a decade and over 1,000 times more powerful in two decades.
That’s like trading in your bicycle for a car and then, shortly after, a rocket ship.
So, instead of dwelling on what today’s ChatGPT can (or can’t) do, look ahead a decade. With vastly more computing power, along with more data and information to draw from, future generations of ChatGPT will possess analytical and problem-solving powers that far exceed current expectations. This revolution will enable tomorrow’s technology to match the diagnostic skills of clinicians today.
2. By emulating how doctors make clinical decisions
Generative AI isn’t a crystal ball. Like Vegas oddsmakers and Wall Street investors, it cannot definitively predict the winner of the World Series or the next stock-market crash.
Instead, ChatGPT and other generative AI apps can access terabytes of data in less than a second (using hundreds of billions of parameters) to “predict” the next best word or idea in a series of words and concepts. But forming sentences is only the beginning.
Generative AI solves problems unlike other AI tools. In fact, it closely resembles how doctors solve problems:
- Begin with a large database. For physicians, data comes from classroom lectures, published research and professional experience. For AI, it’s the totality of digitally published material.
- Extract useful information. A physician will recall (or look up) the relevant information that applies to a patient’s symptoms. Generative AI will use billions of parameters to pinpoint appropriate text.
- Use a predictive process to identify the right pieces. Physicians compare possible diagnoses, whereas today’s ChatGPT tests sentences. Both weigh the options and predict among (all of available possibilities) the best match.
Right now, the biggest difference is that doctors can perform an additional step: asking patients a series of clarifying questions and ordering tests to achieve greater accuracy when drawing conclusions. Next generations of generative AI will be able to complete this step (or at least recommend the appropriate laboratory and radiology tests). Already, Microsoft’s new AI-powered interactive chat feature can ask iterative questions and learn from the conversations.
Just like residents in a hospital, generative AI will initially make mistakes that require a skilled physician to correct. But with greater experience and computing power will come increased acuity and accuracy, as happens with physicians, too. With time, ChatGPT will make fewer errors until it can match or even surpass the predictive powers (and clinical quality) of medical professionals.
3. By providing around-the-clock medical assistance
In the United States, 40% of Americans suffer two or more chronic illnesses, which, as the name implies, affects their health every day.
What these patients need is continuous daily monitoring and care. Unfortunately for them, the traditional office-based, in-person medical system is not set up to provide it. This is where AI can make a tremendous difference.
Unlike a solo doctor, next generations of generative AI will be able to monitor patients 24/7 and provide ongoing medical expertise. Doing so would help patients prevent chronic illnesses like heart disease, hypertension and diabetes, and minimize their deadly complications, including heart attacks, strokes and cancer. This service would cost just pennies a day (ideal at a time when chronic diseases contribute to 90% of all healthcare expenditures).
Generative AI could help patients with chronic disease by:
- Syncing with wearable devices and supportive consumer technologies like Alexa to provide round-the-clock monitoring while giving patients individualized, daily health updates.
- Comparing wearable-device readings against the expected ranges preset by each patient’s doctor—creating patient and physician alerts when something’s wrong.
- Reminding patients at home when they they’re due for preventive screenings, Rx refills or daily exercise (along with other lifestyle improvements).
4. By preventing medical errors
Given OpenAI’s success with Dall-E, an image-based AI platform, along with promising developments in video-based AI from companies like Meta, we can expect machine-learning capabilities will evolve far beyond predicting text.
As an example, video-enabled AI in hospitals could help prevent medical errors, a leading cause of death in the United States.
Lapses in patient safety, especially in hospitals, kill tens of thousands of people annually (with some estimates reaching as high as 200,000 deaths). Scientists have defined the steps needed to prevent these unnecessary fatalities. Yet, too often, doctors and nurses fail to follow evidence-based protocols, leading to avoidable complications.
A recent paper published in the New England Journal of Medicine calculated that nearly 1 in 4 individuals admitted to a hospital will experience harm during their stay. Healthcare pundits have gone so far as to recommend hospitalized people bring a family member with them to protect against deadly mistakes made by humans. That won’t be necessary in the future.
Next generations of ChatGPT with video capability will be able to observe doctors and nurses, compare their actions to evidence-based guidelines and warn clinicians when they’re about to commit an error.
This advancement would prevent nearly all medication errors, as well the majority of hospital acquired: infections, pneumonia and pressure ulcers.
5. By helping all doctors perform like the best
There is an art and a science to medicine. Medical students and residents learn both skills through a combination of textbooks, journal articles, classroom instruction and observation of skilled clinicians. Future generations of AI will follow the same approach.
Once ChatGPT is connected to bedside patient monitors, and can access laboratory data and listen to physician-patient interactions, the application will begin to predict the optimal set of clinical steps. Each time it compares those decisions against the clinical notes and orders of attending physicians in the electronic health record, ChatGPT will learn and improve.
A matriculating first-year medical student needs 10 years of education and training to become fully skilled. Future generations of ChatGPT will complete the process in months or less, learning from the actions of the best clinicians in hundreds of hospitals. And once generative AI becomes sufficiently adept at predicting what experts will do, it can make that expertise available to doctors and nurses anywhere in the country.
What ChatGPT can’t do
No matter how powerful and skilled ChatGPT becomes, it will have limitations. The application will always be dependent on the accuracy of human-inputted data. It will be influenced by the biases of doctors on which the application is trained.
But over time, it will continually improve and address ever-more complex medical problems. Whether that requires 10 years (and 32 times the computing power) or 20 years (and 1,000 times the power), future generations of generative AI will rival and ultimately exceed the cognitive, problem-solving abilities of today’s physicians.
To prepare the next generation of doctors, today’s educators must break healthcare’s unwritten rules and build this technology into medical school and residency training. Rather than viewing ChatGPT as a threat, trainees will benefit by learning to harness the clinical powers of generative AI.
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